Automated Non-Conforming Pixel Masking via Frequency Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Imaging devices face inefficiencies due to non-conforming pixels, which require manual identification and masking by technicians, leading to downtime, inaccurate images, and increased costs.
Innovation Solution
An automated system that identifies non-conforming pixels by analyzing communication events' frequency and masks subsequent events, using a processor to determine if a pixel is non-conforming based on thresholds and masking it to prevent erroneous data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual identification and masking of non-conforming pixels is performed, then pixel errors can be corrected, but imaging downtime increases and operational efficiency decreases
Solution Approach 1:
The imaging device performs self-diagnosis and self-correction by automatically detecting non-conforming pixels through communication event frequency analysis and masking them without external intervention, enabling the system to maintain image accuracy while continuing operation
Solution Approach 2:
The system proactively identifies and masks non-conforming pixels before they significantly degrade image quality by continuously monitoring communication event frequencies and comparing them against threshold values, preventing erroneous data from being captured
2Reliability
If manual masking of non-conforming pixels is performed, then image quality is maintained, but operational costs and technician time increase
Solution Approach 1:
The imaging device automatically maintains image quality through self-diagnosis and self-correction mechanisms, eliminating the need for technician intervention and reducing operational costs while preserving image accuracy
Solution Approach 2:
The system continuously monitors communication event frequencies from pixels and uses feedback loops to automatically adjust by masking non-conforming pixels, maintaining image quality through real-time self-regulation without external control
3Extent of automation
If continuous monitoring of pixel communication events is implemented, then non-conforming pixels are automatically identified, but system complexity increases
Solution Approach 1:
The system replaces manual mechanical inspection and masking procedures with automated electronic monitoring and software-based frequency analysis, using digital signal processing to detect and mask non-conforming pixels without physical intervention
Data Source
AI summary
One embodiment provides a method, including: receiving a plurality of communication events associated with a pixel of an imaging device; identifying a frequency associated with the communication events, wherein the identifying a frequency comprises determining a number of communication events occurring within a predetermined time interval or determining a mean time interval between the communication events; determining, from a plurality of pixels neighboring the pixel, a frequency range comprising an upper frequency limit and a lower frequency limit; resolving, from the identified frequency and the determined frequency range, whether the pixel comprises a non-conforming pixel; and masking, if the pixel comprises a non-conforming pixel, subsequent communication events from the non-conforming pixel. Other aspects are described and claimed.